{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaborative filtering deep dive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai2.collab import *\n",
    "from fastai2.tabular.all import *\n",
    "path = untar_data(URLs.ML_100k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  movie  rating  timestamp\n",
       "0   196    242       3  881250949\n",
       "1   186    302       3  891717742\n",
       "2    22    377       1  878887116\n",
       "3   244     51       2  880606923\n",
       "4   166    346       1  886397596"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
    "                      names=['user','movie','rating','timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_skywalker = np.array([0.98,0.9,-0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user1 = np.array([0.9,0.8,-0.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1420000000000003"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user1*last_skywalker).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "casablanca = np.array([-0.99,-0.3,0.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.611"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user1*casablanca).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning the latent factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie              title\n",
       "0      1   Toy Story (1995)\n",
       "1      2   GoldenEye (1995)\n",
       "2      3  Four Rooms (1995)\n",
       "3      4  Get Shorty (1995)\n",
       "4      5     Copycat (1995)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1',\n",
    "                     usecols=(0,1), names=('movie','title'), header=None)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>875747190</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>883888671</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>879138235</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>876503793</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  movie  rating  timestamp         title\n",
       "0   196    242       3  881250949  Kolya (1996)\n",
       "1    63    242       3  875747190  Kolya (1996)\n",
       "2   226    242       5  883888671  Kolya (1996)\n",
       "3   154    242       3  879138235  Kolya (1996)\n",
       "4   306    242       5  876503793  Kolya (1996)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = ratings.merge(movies)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>332</td>\n",
       "      <td>Jaws 2 (1978)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>343</td>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>624</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>887</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>Liar Liar (1997)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>907</td>\n",
       "      <td>Nell (1994)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>237</td>\n",
       "      <td>Boys of St. Vincent, The (1993)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>683</td>\n",
       "      <td>Apt Pupil (1998)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>243</td>\n",
       "      <td>Piano, The (1993)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>71</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_users  = len(dls.classes['user'])\n",
    "n_movies = len(dls.classes['title'])\n",
    "n_factors = 5\n",
    "\n",
    "user_factors = torch.randn(n_users, n_factors)\n",
    "movie_factors = torch.randn(n_movies, n_factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.4586, -0.9915, -0.4052, -0.3621, -0.5908])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_3 = one_hot(3, n_users).float()\n",
    "user_factors.t() @ one_hot_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.4586, -0.9915, -0.4052, -0.3621, -0.5908])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_factors[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative filtering from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Example:\n",
    "    def __init__(self, a): self.a = a\n",
    "    def say(self,x): return f'Hello {self.a}, {x}.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Sylvain, nice to meet you.'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex = Example('Sylvain')\n",
    "ex.say('nice to meet you')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        return (users * movies).sum(dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 2])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = dls.one_batch()\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DotProduct(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.309138</td>\n",
       "      <td>1.299825</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.102525</td>\n",
       "      <td>1.121799</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.967625</td>\n",
       "      <td>0.993044</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.824581</td>\n",
       "      <td>0.901068</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.796305</td>\n",
       "      <td>0.884824</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        return sigmoid_range((users * movies).sum(dim=1), *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.982041</td>\n",
       "      <td>0.988120</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.879609</td>\n",
       "      <td>0.903623</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.671048</td>\n",
       "      <td>0.866238</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.485887</td>\n",
       "      <td>0.874471</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.362418</td>\n",
       "      <td>0.878503</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProduct(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProductBias(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.user_bias = Embedding(n_users, 1)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        self.movie_bias = Embedding(n_movies, 1)\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        res = (users * movies).sum(dim=1, keepdim=True)\n",
    "        res += self.user_bias(x[:,0]) + self.movie_bias(x[:,1])\n",
    "        return sigmoid_range(res, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.945770</td>\n",
       "      <td>0.936150</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.824700</td>\n",
       "      <td>0.866173</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.589560</td>\n",
       "      <td>0.866441</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.408351</td>\n",
       "      <td>0.893146</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.284422</td>\n",
       "      <td>0.900390</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2,2,100)\n",
    "a_s = [1,2,5,10,50] \n",
    "ys = [a * x**2 for a in a_s]\n",
    "_,ax = plt.subplots()\n",
    "for a,y in zip(a_s,ys): ax.plot(x,y, label=f'a={a}')\n",
    "ax.set_ylim([0,5])\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.935149</td>\n",
       "      <td>0.957065</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.859534</td>\n",
       "      <td>0.872719</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.734857</td>\n",
       "      <td>0.832345</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.600854</td>\n",
       "      <td>0.820575</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.500854</td>\n",
       "      <td>0.820172</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting embeddings and biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
       " 'Children of the Corn: The Gathering (1996)',\n",
       " 'Home Alone 3 (1997)',\n",
       " 'Robocop 3 (1993)',\n",
       " 'Beautician and the Beast, The (1997)']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.model.movie_bias.weight.squeeze()\n",
    "idxs = movie_bias.argsort()[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['L.A. Confidential (1997)',\n",
       " 'Shawshank Redemption, The (1994)',\n",
       " 'Silence of the Lambs, The (1991)',\n",
       " \"Schindler's List (1993)\",\n",
       " 'Titanic (1997)']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs = movie_bias.argsort(descending=True)[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ratings.groupby('title')['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_idxs = tensor([learn.dls.classes['title'].o2i[m] for m in top_movies])\n",
    "movie_w = learn.model.movie_factors.weight[top_idxs].cpu().detach()\n",
    "movie_pca = movie_w.pca(3)\n",
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
    "idxs = list(range(50))\n",
    "X = fac0[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(top_movies[idxs], X, Y):\n",
    "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_factors = learn.model.movie_factors.weight\n",
    "la_idx = dls.classes['title'].o2i['L.A. Confidential (1997)']\n",
    "distances = nn.CosineSimilarity(dim=1)(movie_factors, movie_factors[la_idx][None])\n",
    "idx = distances.argsort(descending=True)[1]\n",
    "dls.classes['title'][idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boot strapping a collaborative filtering model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using fastai.collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.950121</td>\n",
       "      <td>0.955827</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.860315</td>\n",
       "      <td>0.877788</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.728865</td>\n",
       "      <td>0.842400</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.594641</td>\n",
       "      <td>0.824307</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.499862</td>\n",
       "      <td>0.825145</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep learning for collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@delegates(TabularModel)\n",
    "class EmbeddingNN(TabularModel):\n",
    "    def __init__(self, emb_szs, layers, **kwargs):\n",
    "        super().__init__(emb_szs, layers=layers, n_cont=0, out_sz=1, **kwargs)\n",
    "        \n",
    "    def forward(self, x): return super().forward(x, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sidebar: kwargs and delegates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End sidebar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(944, 50), (1635, 50)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embs = get_emb_sz(dls, {'user':50, 'title':50})\n",
    "embs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = EmbeddingNN(embs, layers=[100], y_range=(0,5.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.965729</td>\n",
       "      <td>0.990180</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.898545</td>\n",
       "      <td>0.914952</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.874419</td>\n",
       "      <td>0.888544</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.817418</td>\n",
       "      <td>0.871285</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.761706</td>\n",
       "      <td>0.877536</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "split_at_heading": true
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
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